Per aspera ad astra: Through complex population modeling to predictive theory

Christopher J. Topping*, Hugo Fjelsted Alrøe, Katharine N. Farrell, Volker Grimm

*Corresponding author for this work

Research output: Other contribution

13 Citations (Scopus)

Abstract

Population models in ecology are often not good at predictions, even if they are complex and seem to be realistic enough. The reason for this might be that Occam’s razor, which is key for minimal models exploring ideas and concepts, has been too uncritically adopted for more realistic models of systems. This can tie models too closely to certain situations, thereby preventing them from predicting the response to new conditions. We therefore advocate a new kind of parsimony to improve the application of Occam’s razor. This new parsimony balances two contrasting strategies for avoiding errors in modeling: avoiding inclusion of nonessential factors (false inclusions) and avoiding exclusion of sometimes-important factors (false exclusions). It involves a synthesis of traditional modeling and analysis, used to describe the essentials of mechanistic relationships, with elements that are included in a model because they have been reported to be or can arguably be assumed to be important under certain conditions. The resulting models should be able to reflect how the internal organization of populations change and thereby generate representations of the novel behavior necessary for complex predictions, including regime shifts.

Original languageEnglish
Number of pages6
Edition5
Volume186
DOIs
Publication statusPublished - 2015

Publication series

NameAmerican Naturalist
PublisherUniversity of Chicago
ISSN (Print)0003-0147

Keywords

  • Agent-based models
  • Complexity
  • Error avoidance
  • Model development
  • Modest approach

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